Why Facebook and the NSA love graph databases

An IT industry analyst article published by SearchDataCenter.

Graph databases play six degrees of separation to find real connections. See how IT teams can use the database approach for businesses.


article_Why-Facebook-and-the-NSA-love-graph-databases
Is there a benefit to understanding how your users, suppliers or employees relate to and influence one another? It’s hard to imagine that there is a business that couldn’t benefit from more detailed insight and analysis, let alone prediction, of its significant relationships.

If you have ever drawn dots on a whiteboard and then connected them, you can appreciate that thinking in terms of nodes and links naturally echoes many real-world scenarios. Many of today’s hottest data analysis opportunities for optimization or identifying fraud are represented as a linked web.

Analyzing sets of nodes and the relationships between them is known as graph theory. In a graph database, a common query might be to find all the related objects, based on a certain pattern of relationship, between two and six links away.

Specialized graph databases are a small but fast-growing part of the so-called NoSQL (not only structured query language) database movement. Graph databases are designed to help model and explore a web or graph of relationships in a natural and more productive way than through the traditional relational database approach.

In a graph database, for example, a common query might be to find all the related objects, based on a certain pattern of relationship, between two and six links away. If the same problem was force-fit into normalized tables in an SQL relational model, the translated query would become quite complex and require tens or even hundreds of nested full table joins.

In a relational database query, every required join is going to cause a performance hit. For graph problems of any size, a SQL approach will be demonstrably slower, more complex, prone to error and definitely not as scalable.

Graph databases don’t require a predefined schema; nodes and links can have attributes edited or assigned to them at any time. If a new relationship type is discovered, it can be added to the database dynamically, extending what’s modeled in the database.

In production, IT should be aware of differences in how graph databases scale, how they use memory and how they ingest (and index) data loads.

…(read the complete as-published article there)